Mathematical Modelling of Malaria Integrating Temperature, Rainfall, and Vegetation Index

The Normalized Difference Vegetation Index (NDVI) distribution and its seasonal cycle were investigated in relation to temperature and precipitation over Siberia and its surrounding regions. The analyses used 5‐year (1987–1991) monthly means. The monthly mean NDVI was calculated from the third‐generation monthly Global Vegetation Index (GVI) product; monthly temperature and precipitation at 611 stations were calculated from Global Daily Summary (GDS) data.The 611 stations were classified by cluster analysis into 10 classes based on the NDVI seasonal cycle (March–October). The geographical distribution characteristics of the NDVI cycle were described using temperature, precipitation and Olson's land‐cover type. In northern regions, where tundra vegetation prevails and temperatures and precipitation are low, the amplitude of the NDVI seasonal cycle is small. In southern regions, where temperatures are high and there is little precipitation, the seasonal amplitude of the NDVI is small because of the arid land type. Forested regions were split into six classes, each characterized by large amplitudes in the NDVI seasonal cycle. The phenological characteristics of the forest classes were noted. For example, a forest‐class localized near Lake Baikal shows higher NDVI values, even with the presence of snow cover in March, compared with other regions. This high NDVI value suggests that the exposed green canopy of the coniferous forest can be observed even when snow is present. In addition, the NDVI peaks at stations near 60°N, where the maximum monthly temperature is around 18°C. This result suggests that the optimum temperature‐precipitation environment coincides to the area in Siberia where the maximum monthly temperature is 18°C. Copyright © 2001 Royal Meteorological Society

Normalized difference vegetation index (NDVI) data, obtained from remote sensing information, are essential in the Shuttleworth-Wallace (S-W) model for estimation of evapotranspiration. In order to study the effect of temporal resolution of NDVI on potential evapotranspiration (PET) estimation and hydrological model performance, monthly and 10-day NDVI data set were used to estimate potential evapotranspiration from January 1985 to December 1987 in Huangnizhuang catchment, Anhui Province, China. The differences of the two calculation results were analyzed and used to drive the block-wise use of the TOPMODEL with the Muskingum-Cunge routing (BTOPMC) model to test the effect on model performance. The results show that both annual and monthly PETs estimated by 10-day NDVI are lower than those estimated by monthly NDVI. Annual PET from the vegetation root zone (PETr) lowers 9.77%–13.64% and monthly PETr lowers 3.28%–17.44% in the whole basin. PET from the vegetation interception (PETi) shows the same trend as PETr. In addition, temporal resolution of NDVI has more effect on PETr in summer and on PETi in winter. The correlation between PETr as estimated by 10-day NDVI and pan measurement (R2 = 0.835) is better than that between monthly NDVI and pan measurement (R2 = 0.775). The two potential evapotranspiration estimates were used to drive the BTOPMC model and calibrate parameters, and model performance was found to be similar. In summary, the effect of temporal resolution of NDVI on potential evapotranspiration estimation is significant, but trivial on hydrological model performance.

A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data—the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data—to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km × 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1° × 1° latitude–longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network. Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40°C for monthly minimum temperatures, near 0.25°C for monthly mean temperatures, and near 0.10°C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near −1.1°C for rural stations to 2.4°C for stations from the largest urban areas. There are some regions of the United States where a regional NDVI value based on a 1° × 1° latitude–longitude area will not represent a typical “rural” NDVI value for the given region, Thus, for some regions of the United States, the urban bias of this study may underestimate the actual current urban bias. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available. It is anticipated that results from global application will provide insights into the urban bias of the global temperature record.

Analysis of a temperature- and rainfall-dependent model for malaria transmission dynamics

In this study, we identify the main modes of variability of the Normalized Difference Vegetation Index (NDVI) and their relationships with precipitation and temperature variations across northern Patagonia (36°–45° S). In this approach, we combined a recently developed high-resolution gridded dataset (20 × 20 km) for temperature and precipitation with a re-scaled NDVI grid to spatially match the climate database. Climate–vegetation relationships were analyzed taking into account a wide range of temporal variations (intra- to inter-annual) of both climate and NDVI. An Empirical Orthogonal Function analysis performed on NDVI delimits four regions that are spatially consistent with previous vegetation classifications for northern Patagonia. In addition, these coherent NDVI regions show similarities with the spatial precipitation patterns and the temporal evolution of precipitation over the common period 2001–2010. Both NDVI and precipitation show evident annual cycles over the Mediterranean climatic region in northwestern Patagonia. These annual cycles decrease in amplitude toward the eastern arid rangelands, and to the south on the evergreen all-year-round rainforests. Significant positive relationships between monthly precipitation and NDVI are recorded in the dry temperate rangelands in northeastern Patagonia. In contrast, direct associations between monthly NDVI and precipitation were absent in the Central Patagonia cold grasslands, where seasonal interactions between precipitation, temperature and NDVI appear to be more relevant. Relationships between NDVI and temperature are generally weaker east of the Andes, but significantly positive in late winter/spring over the temperate forests in western North Patagonia. Our results indicate that climate–NDVI relationships in northern Patagonia are biome specifics with the occurrence of temporal lags and precipitation–temperature interactions in the responses of vegetation to climate at some ecosystems.

Soil Impact on Satellite Based Vegetation Monitoring in Sahelian Mali

The spatial information on the invasion of alien plant species is considered necessary to develop policies aimed at management since there are significant threats to local biodiversity. The present study was conducted in the Salalah plains, located in the Southern part of Oman, and is severely affected by the invasion of Prosopis juliflora. Spatial analyses of the extent of invasive colonies along with the rate of spread were carried out. The impact of the meteorological parameters, precipitation, temperature, and relative humidity, on the growth dynamics of P. juliflora was also carried out. During the study period of approximately three decades from 1984 to 2013, more than 100 hectares of the natural land cover were converted into mono-dominant stands of P. juliflora. Although different programs to cut the P. juliflora were organized in the study area between 2000 and 2002, these programs have contributed unintentionally to the spread of the seeds and cuttings over larger areas amplifying the rate of increase from 1 ha per year to more than 9 ha. The photosynthetic activity of P. juliflora is directly affected by relative humidity and precipitation. Linear regression analysis showed a significant negative relationship between cumulative monthly precipitation and Normalized Difference Vegetation Index (NDVI) with an r-squared value of 0.59 (r = − 0.77) and between mean monthly relative humidity and NDVI with an r-squared value of 0.63 (r = − 0.80). At the same time, there was no relationship between mean monthly temperature and NDVI. Cross-correlation analysis showed that P. juliflora’s vigor was sensitive to the previous month’s high relative humidity and precipitation in a negative way. The adverse effects of precipitation and relative humidity on the growth dynamics of P. juliflora, especially during the monsoon season, should be taken into consideration when planning for integrated prevention and control programs of the invasive species.

Since the initiation of the Grain for Green Project (GFGP) in 1999, dramatic change in vegetation status on the Loess Plateau. Spatially, geographical detector was employed to detect dominant variables influencing the spatial arrangement of normalized difference vegetation index (NDVI). Temporally, lagged or accumulated monthly precipitation, temperature and standardized precipitation evapotranspiration indices (SPEIs) sensitive to the monthly NDVI were first detected for every individual pixel, and the correlation between the NDVI and meteorological elements with time-lag effects was established a random forest model of unchanged land cover, followed by attributing impacts of climatic alterations and human interventions through residual examination across changed land cover. The findings indicate that (1) precipitation, slope, and soil dominantly influence the spatial arrangement of the NDVI. (2) Precipitation in current the month and cumulative temperatures of the previous 1–2 months steadily affect vegetation growth significantly, the optimal accumulation time interval for SPEI around 2000 are 8 months and 4 months, respectively. (3) Increases in the average NDVI within woodland and meadow vegetation on the Loess Plateau were primarily driven by climate change before 2000, accounting for 76.2%, whereas after 2000 it was dominantly driven by human activities, accounting for 64.16%.

Global discrimination of land cover types from metrics derived from AVHRR pathfinder data

The global land-surface temperature (LST) and normalized difference vegetation index (NDVI) products retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data in 2001 were used in this study. The yearly peak values of NDVI data at 5km grids were used to define six NDVI peak zones from -0.2 to 1 in steps of 0.2, and the monthly NDVI values at each grid were sorted in decreasing order, resulting in 12 layers of NDVI images for each of the NDVI peak zones. The mean and standard deviation of daytime LSTs and day-night LST differences at the grids corresponding to the first layer of NDVI images characterize the thermal status of terrestrial ecosystems in the NDVI peak zones. For the ecosystems in the 0.8-1 NDVI peak zone, daytime LSTs distribute from 0-35 °C and day-night LST differences distribute from -2 to 22 °C. The daytime LSTs and day-night LST differences corresponding to the remaining layers of NDVI images show that the growth of vegetation is limited at low and high LSTs. LSTs and NDVI may be used to monitor photosynthetic activity and drought, as shown in their applications to a flood-irrigated grassland in California and an unirrigated grassland in Nevada.

Different vegetation indices from satellite images have been used for monitoring drought damages, and this study aimed to develop a drought index using NOAA/AVHRR NDVI(Normalized Difference Vegetation Index) and to analyze the temporal and spatial distribution of spring drought severity in North Korea from 1998 to 2001. A new drought index, DevNDVI(Deviation of NDVI), was defined as the difference between a monthly NDVI and average monthly NDVI at the same cover area, and the DevNDVI images at all years except for 2001 demonstrated the drought-damaged areas referred from various domestic and foreign publications. The vegetation of 2001 showed high vitality despite the least amount of rainfall among the target years, and the reason was investigated that higher temperature above normal average would shift the growing stages of plants ahead. Therefore, complementary methods like plant growth models or ground survey data should be adopted in order to evaluate drought-induced plant stress using satellite-based NDVI and to make up far the distortion induced by other environments than lack of precipitation.

A study was undertaken to investigate the possibility of using the Normalised Difference Vegetation Index (NDVI) data developed by the National Oceanic Atmospheric Administration (NOAA's), Very High Resolution Radiometer (VHRR) to predict cotton yield in Swaziland. Fourteen years of historical data of average cotton yield and NDVI were used for the study. A regression analysis was performed for 504 data set for NDVI and 14 data set for cotton yield. A linear model using the cumulative NDVI was developed to predict cotton yield. The regression analysis was performed on minimum, average and maximum NDVI. The cumulative maximum NDVI was found to be the most effective in forecasting cotton yield than either the minimum or the average NDVI despite the significance of the minimum NDVI. The regression coefficient for the cumulative maximum NDVI was 70%, while that for the minimum NDVI was 56%. The best regression period was found to be from the second dekad of February to the second dekad of April, which coincides with the flowering and boll formation stages of cotton. This means that a production forecast can be issued about 1-2 months before harvest. Such information would be very useful for early warning purposes. Although very good results were achieved from this study it has to be noted that all models have limitations. Satellites are affected by various atmospheric effects, which may compromise the quality of the data produced. However, if limitations are considered when using satellite data very useful information can be derived from satellite developed data. Due to the limited number of years (14 years) used in this study the model has to be further tested, calibrated and updated as more yield and NDVI data become available. (UNISWA J Agric:2000 9: 13-21)

A new mechanistic deterministic model for assessing the impact of temperature variability on malaria transmission dynamics is developed. Sensitivity and uncertainty analyses of the model parameters reveal that, for temperature values in the range 16–[Formula: see text]C, the three parameters with the greatest influence on disease dynamics are the mosquito carrying capacity, transmission probability per contact for susceptible mosquitoes and human recruitment rate. This study emphasizes the combined use of mosquito-reduction strategies and personal protection against mosquito bites during periods when the mean monthly temperatures are in the range 16.7–25[Formula: see text]C. For higher monthly mean temperatures in the range 26–34[Formula: see text]C, mosquito-reduction strategies should be emphasized ahead of personal protection. Numerical simulations of the model reveal that mosquito maturation rate has a minimum sensitivity (to the associated reproduction threshold of the model) at 24[Formula: see text]C and maximum at 30[Formula: see text]C. The mosquito biting rate has maximum sensitivity at 26[Formula: see text]C, while the minimum value for the transmission probability per bite for susceptible mosquitoes occurs at 24[Formula: see text]C. Furthermore, it is shown, using mean monthly temperature data from 67 cities across the four regions of sub-Saharan Africa, that malaria burden (measured in terms of the total number of new cases of infection) increases with increasing temperature in the range 16–28[Formula: see text]C and decreases for temperature values above 28[Formula: see text]C in West Africa, 27[Formula: see text]C in Central Africa, 26[Formula: see text]C in East Africa and 25[Formula: see text]C in South Africa. These findings, which support and complement a recent study by other authors, reinforce the potential importance of temperature and temperature variability on future malaria transmission trends. Further simulations show that mechanistic malaria transmission models that do not incorporate temperature variability may under-estimate disease burden for temperature values in the range 23–27[Formula: see text]C, and over-estimate disease burden for temperature values in the ranges 16–22[Formula: see text]C and 28–32[Formula: see text]C. Additionally, models that do not explicitly incorporate the dynamics of immature mosquitoes may under- or over-estimate malaria burden, depending on mosquito abundance and mean monthly temperature profile in the community.

Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change

Wetness and warmth are the principal factors that control global vegetation distribution. This paper investigates climate–vegetation relationships at a global scale using the normalized difference vegetation index (NDVI), warmth index (WAI), and wetness index (WEI).The NDVI was derived from a global, 20‐year Advanced Very High Resolution Radiometer (AVHRR) dataset with 4‐min resolution. The WEI was defined as the ratio of precipitation to potential evaporation. The WAI was defined as the cumulative monthly mean temperature that exceeds 5 °C annually. Meteorological data from the International Satellite Land‐Surface Climatology Project Initiative II (ISLSCP II) dataset were used to calculate the WEI and WAI. All analyses used annual values based on averages from 1986 to 1995 at 1 × 1 degree resolution over land. Relationships among NDVI, WEI, and WAI values were examined using a vegetation‐climate diagram with the WEI and WAI as orthogonal coordinates.The diagram shows that large NDVI values correspond to areas of tropical and temperate forests and large WEI and WAI values. Small WEI and WAI values are associated with small NDVI values that correspond to desert and tundra, respectively.Two major regimes are revealed by the NDVI vegetation‐climate diagram: wetness dominant and warmth dominant. Wetness dominates mid‐ and low latitudes. Warmth dominates high latitudes north of 60°N or elevated land such as the Tibetan Plateau. The boundary between the two regimes roughly corresponds to the vegetation boundary between taiga forest and southern vegetation. Over northern Eurasia, the boundary occurs in areas where the NDVI is large and the maximum monthly temperature is around 18 °C. Copyright © 2006 Royal Meteorological Society.